In automation systems engineering, signals are considered as common concepts for linking information across different engi-neering disciplines, such as mechanical, electrical, and software engineering. Signal engineering is facing tough challenges in managing the interoperability of heterogeneous data tools and models of each individual engineering discipline, e.g., to make signal handling consistent, to integrate signals from heterogene-ous data models/tools, and to manage the versions of signal changes across engineering disciplines. Currently, signal changes across engineering disciplines are primarily managed manually which is costly and error-prone. The main contribution of this paper is the signal change management process model as an input for semantic integration of engineering tools and models to sup-port (semi) automated signal change management. Major result was that the process model discovery approach well supports the discovery of semantic integration requirements across heteroge-neous engineering tools and models more efficient compared to the manual signal change management.